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HVDC GIS/GIL – classification of PD defects using NoDi* pattern and machine learning

HVDC GIS/GIL – classification of PD defects using NoDi* pat...

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22nd international Symposium on High Voltage engineering (ISH 2021)

作者： B. Schober U. Schichler Institute of High Voltage Engineering and System Performance Graz University of Technology Inffeldgasse 18 8010 Graz Austria

Partial discharge measurement is one of the most important diagnosis methods for high voltage equipment. For equipment under AC voltage exists reliable interpretation tools such as the PRPD pattern. Due to the lack of phase information at DC voltage, many of the established methods for interpreting PD cannot be used. At DC voltage, the classification of PD defects can be realised with NoDi* pattern or automatically with machine learning algorithms. For this purpose, features are extracted from the measured PD data and provided to the trained algorithm. Five typical PD defects of HVDC GIS/GIL were investigated – bouncing particles, particles in firefly effect, floating electrodes, and chamber and busbar corona. New aspects of floating electrode with respect to the dynamic range of the input amplifier of the PD measurement system were investigated. For this purpose, two measurement systems in parallel with different gain settings were used. Furthermore, the possibility to distinguish between busbar and chamber corona at DC voltage was examined. The extracted features for the machine learning algorithms were analysed and both manual and automatic feature selection were performed and compared to verify the manual feature selection. The results were in good agreement. In a next step, the necessary number of PD pulses was investigated to obtain the same seperability between the PD defects, whereas a number of 500 PD pulses seems sufficient for a PDM system.

关键词： busbars corona feature extraction gas insulated switchgear gas insulated transmission lines learning (artificial intelligence) partial discharge measurement pattern classification power engineering computing feature selection

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Dual-loss path CNNs fusion architecture for image restoration with scattered data 22

Dual-loss path CNNs fusion architecture for image restoratio...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Xu, Linli Han, Jing Bai, Lianfa Nanjing University of Science and Technology Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing210094 China

ISBN: (纸本)9781728176499

High-resolution scattered data restoration is vital for series analysis of imaging through scattering medium. However, the existence of traditional methods mostly using deterministic transmission matrix, which limits the speckle de-correlations. Thus, learning based networks are applied to do image restoration through different random scattering mediums. In this work, we develop a novel dual-loss path networks fusion architecture for scattered image restoration focusing on improving reconstructed image details and the edge information. Our architecture consists of dual-loss path of convolution neural network (CNN) modules. In particular, we use the perceptual loss to extract the high level feature images, and the objective loss to extract the low level feature images, individually. After the both low level and high level feature manners are reconstructed by dual-path CNNs model, we use our proposed fusion method to combine them in frequency domain. Our network architecture is verified effective on two kinds of public datasets (MINST and Fashion-MINST). The experimental results show that our proposed method achieving better restoration than common CNN methods especially in the edges and the corner information of the predicted images. © 2020 IEEE.

关键词： Image fusion

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Education 4.0-A New Systemic Paradigm of Teacher Education for Technicians-Engineers in the Czech Republic and Slovakia 2nd

Education 4.0-A New Systemic Paradigm of Teacher Education f...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Andres, Pavel Hrmo, Roman Czech Tech Univ Masaryk Inst Adv Studies Prague Czech Republic DTI Univ Dubnica Nad Vahom Slovakia

ISBN: (纸本)9783030402716;9783030402709

The *** Curriculum should be based on the socioeconomic and cultural needs of the information society, on the demands of school policy, and on the reflection of transformations in the field of education and training. The key objective is to develop the curriculum in line with the demands of a knowledge-based economy. This economy needs qualified secondary school teachers who will be able to prepare the young generation for the technological demands of industry 4.0, for the labor market and for further education. The concept of development includes both the international dimension and the knowledge of the national system with the so-called dual system of education, i.e. the emphasis placed on intensive cooperation of secondary vocational schools and employers. Based on this knowledge, the traditional vision of preparing future teachers should be overcome and a future perspective should be predicted.

关键词： engineering pedagogy Curriculum development IGIP criteria for (re)accreditation Education 4.0

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A Deep learning Framework Based on Spatio-Temporal Attention Mechanism for Traffic Prediction 22

A Deep Learning Framework Based on Spatio-Temporal Attention...

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作者： Hu, Jun Li, Bo Hunan University College of Computer Science and Electronic Engineering Changsha China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781728176499

Traffic prediction is a significant unit of intelligent transportation systems in smart cities. For instance, a precise vehicle prediction model can not only help the city allocate resources in advance to meet travel needs but also reduce traffic congestion and energy waste caused by roadside vehicles. For traffic prediction tasks, how to model complex spatial dependencies and temporal dynamics is still an important issue. Although these two factors are considered in the modeling, the currently existing work mainly captures the spatial dependence by extracting the spatial features of the region, which ignores the mutual influence between the regions. Besides, they usually assume that temporal dynamics are strictly periodic. However, the mutual influence between regions has an impact on the prediction, and the temporal dynamics can be affected by some short-Term mutation information (e.g., weather conditions), which will cause temporal dynamics to be perturbed from one period to another. To solve these two problems, this paper proposes a novel Spatio-Temporal Attention Mechanism-Based Dynamic Network (STADN), where via combining spatial attention mechanism and flow gating mechanism to learn dynamic spatial dependencies, via combining periodic shifting attention mechanism and a new transformation-gated LSTM (NTG-LSTM) to solve the long-Term periodic temporal shifting caused by short-Term mutation information. We prove the effectiveness of this method through the experimental results on real-world traffic datasets. © 2020 IEEE.

关键词： Traffic congestion

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Enhancing Social Ties Through Manual Player Matchmaking in Online Multiplayer Games 22nd

Enhancing Social Ties Through Manual Player Matchmaking in O...

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22nd international conference on Human-Computer Interaction,HCII 2020

作者： Riyadh, Md Arya, Ali Chan, Gerry Imran, Masud Carleton School of Information Technology Interactive Media Group Carleton University Ottawa Canada Department of Software Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (纸本)9783030601270

Beyond skill and performance, there is a large social aspect to online multiplayer games that contributes to the overall gameplay experience. To complement this experience, player matchmaking techniques in those games have evolved from using only simple skill-based data to integrating multitude of different data such as social data, player personality etc. However, these techniques still lack sensitivity to this social aspect of multiplayer gaming. Modern player matchmaking techniques are dominated by automatic matchmaking algorithms which do not provide a way to the players to manually recommend one friend to another. We identify this as a limitation of the existing player matchmaking techniques which restricts players’ ability to leverage their existing ties to enhance their gameplay experience, and perhaps, also deprives them of extending their social ties through online games. We propose that the ability to perform such manual recommendation within online multiplayer games can strengthen the social aspect of online gaming and enhance the overall gameplay experience. © 2020, Springer Nature Switzerland AG.

关键词： Social aspects

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The Needs-Oriented Approach of the Dresden School of engineering Pedagogy and Education 2nd

The Needs-Oriented Approach of the Dresden School of Enginee...

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作者： Gormaz-Lobos, Diego Galarce-Miranda, Claudia Hortsch, Hanno Kersten, Steffen Univ Talca Talca Chile Univ Talca CIEI IGIP Zentrum Talca Chile Tech Univ Dresden Int Soc Engn Pedag IGIP Dresden Germany Tech Univ Dresden Dresden Germany

ISBN: (纸本)9783030402747;9783030402730

This paper presents different aspects about the Needs-Oriented Approach on engineering Pedagogy and Education developed since 1951 at the Technische Universitat Dresden (Germany). Prof. Hans Lohmann founded in 1951 the engineering Pedagogy Institute at the TU Dresden in his quest to systematize and to professionalize at an institutional level the teaching and research in engineering. engineering Pedagogy is an interdisciplinary scientific subject that collect the "needs" and "demands" of: engineering and technical sciences, pedagogy and didactic and the education system in itself (see Lohmann 1954;Melezinek 1999). Lohmann's work is continued at the present at the Institute for Vocational Education at TU Dresden, in charge of Prof. Hanno Hortsch (currently President of IGIP), who leaded many research projects in engineering education in Germany and another countries. Kersten (2015) proposes a scheme that describes the factors that influence and condition the engineering Education: (i) the economic sector, (ii) engineering sciences, (iii) society, and (iv) the student (see Kersten et al. 2015). The results of two surveys show the (1) "needs" of academic training related to the different pedagogical aspects, and (2) "needs" for different knowledge, skills and technological tools for the teaching in engineering careers in relation to current and future industrial requirements. A Needs-Oriented engineering Pedagogy seeks to establish a teaching and learning process that is better focused on the context where it develops.

关键词： engineering Pedagogy Needs-Oriented engineering Pedagogy Needs-oriented engineering education Dresden School of engineering Pedagogy and Education

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Generating an image training dataset for edges detection on an image using neural networks 22

Generating an image training dataset for edges detection on ...

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22nd international conference on Digital Signal Processing and its Applications, DSPA 2020

作者： Alpatov, Boris A. Shubin, Nikita Yu. Yakovlev, Andrey V. Ryazan State Radio Engineering University Department of Automation and Information Technologies in control Ryazan Russia

ISBN: (数字)9781728175287

ISBN: (纸本)9781728175287

One of the cornerstones of machine learning is training data. In case of the insufficient data, correct generalization becomes difficult. This problem is particularly serious when artificial neural networks are used. This work is devoted to an algorithm for generating parameters of arbitrarily shaped edges and creating images containing these edges. Part of the calculations can be performed at the GPU. It accelerates the overall generation more than 3 times. The dataset created in this way can be used in training artificial neural networks for the edge detection task. © 2020 IEEE.

关键词： Machine learning

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A Deep-Shallow Network for Passive Underwater Target Recognition 22

A Deep-Shallow Network for Passive Underwater Target Recogni...

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作者： Song, Gaoyu Liu, Xingang Zeng, Xin Luo, Hengguang Wang, Dayu Zhang, Boxuan University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China ESCC the 54th Research Institute of CETC Shijiazhuang China

ISBN: (纸本)9781728176499

Passive sonar system is an essential method for underwater target recognition. However, it is also a challenging task due to the interference of ambient noise and multiple operative conditions of different targets. In this paper, a weighted voting mechanism based Deep-shallow Network (DSN) is proposed. It utilizes both Low-Frequency Acquisition and Ranging (LOFAR) spectrum and Mel Frequency Cepstrum Coefficient (MFCC) spectrum as input features. Besides, part of the model training process is optimized with the idea of transfer learning. Finally, we proposed a weighted voting mechanism to take advantage of convolutional neural network (CNN) and residual network (ResNet) at the same time so that shallow and deep information could be integrated. Experiments show that the highest recognition accuracy achieves 93.3% which is at least 6.1% higher than traditional machine learning methods. © 2020 IEEE.

关键词： Convolutional neural networks

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M-learning as Support Tool in the Diffusion of the Traditional Food: Case Study Ambato 2nd

M-learning as Support Tool in the Diffusion of the Tradition...

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作者： Paez-Quinde, Cristina Torres-Onate, Francisco Viteri, Maria-Fernanda Porras, Maria-Emilia Univ Tecn Ambato Fac Ciencias Humanas & Educ Ambato Ecuador

ISBN: (纸本)9783030402747;9783030402730

The present investigation has as main objective to evaluate the impact that mobile technologies have about the current diffusion of the Ambato's gastronomy, starting from the origins, changes and contributions through the history of the main ingredients that form part of the city's traditional menu. Also the contribution of identity to the place, until the verification of the acceptability level that the different traditional culinary options have within the locals, that are subject to a promotion coupled to a technological society that expects to have all its activities and interests available through the use of mobile devices. This research was based on the ADDLE methodology, for the application development, which was also validated through the UTAUT technology acceptability instrument. Considering that the application had two approaches that were both the technological, and educational, the statistic used for this research was Friedman, since when the mobile application was developed, it was applied to the population having a pretest and a posttest with a final result that allowed accepting the alternate hypothesis proposed in the investigation. The result of the project allowed to use an auxiliary technological tool to boost the interest and knowledge that the current generations have regarding the traditional gastronomy of Ambato city through the distribution of culinary information in reference to the preparation processes of the different dishes in the local traditional menu that can strength the cultural identity of Ambato's society in a practical and didactic manner.

关键词： Culture Traditional food learning Mobile technology Gastronomic diffusion Applications

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A drug information embedding method based on graph convolution neural network 22

A drug information embedding method based on graph convoluti...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Feng, Xiaoyi Peng, Shaoliang Li, Fei Xu, Ying Zeng, Xiangxiang Wei, Dong-Qing Liu, Yunhao College of Computer Science and Electronic Engineering Hunan University China Beijing Institute of Radiation Medicine Department of Biotechnology China Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences Ministry of Education Shanghai Jiao Tong University China Peng Cheng Lab Shenzhen518000 China

ISBN: (纸本)9781728162676

New drug development is an extremely time-consuming and high-risk process. [1]It has been widely valued by the biomedical industry to fully explore the new uses of existing drugs and reorientate them. [2]How to find drug disease with potential therapeutic relationship from a large number of unproven relationship pairs is the research focus of drug reorientation. With the help of machine learning model, we can improve the enrichment degree of potential drug disease relationship pairs, and reduce the false positive rate of prediction. In the past few years, a series of graph based convolutional network models have been developed to calculate the information latent feature representation of nodes and links. Researchers at home and abroad have done a lot of research on network embedding technology based on biomedical data, and have achieved a series of important research results. Among them, the research methods used can be divided into two categories: one is the traditional machine learning algorithm based on artificial feature extraction, the other is the method based on deep learning. For example, kipf and welling [3]proposed a new graph convolution network (GCN) with parts of existing models, DeepDR [4] and DTINet [5] based on node characteristics and their connections, which can be used for node classification. Aiming at the problem of imbalance of drug information data samples, the invention provides a drug relocation method based on deep learning multi-source heterogeneous network. In order to avoid the limitations of traditional feature extraction methods, such as highly dependent on the experience and knowledge of medical staff, strong subjectivity, consuming a lot of time and energy to complete, and extracting high-quality features with distinguishing features often exists In this paper, with the help of graph convolution encoder model and variational auto encoder neural network, we can automatically learn the characteristics of multi-source and heterogeneous

关键词： Heterogeneous networks

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